Total 54,390 skills, Data Processing has 2786 skills
Showing 12 of 2786 skills
Salesforce Data Cloud Harmonize phase. TRIGGER when: user works with DMOs, mappings, relationships, identity resolution, unified profiles, data graphs, or universal IDs. DO NOT TRIGGER when: the task is only about streams/DLOs (use sf-datacloud-prepare), segments/insights (use sf-datacloud-segment), retrieval/search (use sf-datacloud-retrieve), or STDM/session tracing (use sf-ai-agentforce-observability).
Use this skill when the user wants to add or update metadata in DataHub: descriptions, tags, glossary terms, ownership, deprecation, domains, data products, structured properties, documents, or field-level metadata. Triggers on: "add tag to X", "update description for X", "set owner of X", "add glossary term", "deprecate X", "create a domain", "create a glossary term", "add a document", or any request to modify DataHub metadata.
Generates a Jupyter notebook that transforms datasets between ML schemas for model training or evaluation. Use when the user says "transform", "convert", "reformat", "change the format", or when a dataset's schema needs to change to match the target format — always use this skill for format changes rather than writing inline transformation code. Supports OpenAI chat, SageMaker SFT/DPO/RLVR, HuggingFace preference, Bedrock Nova, VERL, and custom JSONL formats from local files or S3.
Build and deploy new Goldsky Turbo pipelines from scratch. Triggers on: 'build a pipeline', 'index X on Y chain', 'set up a pipeline', 'track transfers to postgres', or any request describing data to move from a chain/contract to a destination (postgres, clickhouse, kafka, s3, webhook). Covers the full workflow: requirements → dataset selection → YAML generation → validation → deploy. Not for debugging (use /turbo-doctor) or syntax lookups (use /turbo-pipelines).
Transform, filter, reshape, join, and manipulate football data. Use when the user needs to clean data, merge datasets, convert between formats, handle missing values, work with large datasets, or do any data manipulation task on football data.
Calculate derived football metrics and models. Use when the user wants to compute xG, xGOT, PPDA, passing networks, expected threat, possession value, pressing intensity, or any derived football statistic from raw data.
Smart CSV importer with format auto-detection. Handles major banks in Canada and US, plus payment platforms (Stripe, PayPal, Wise, WeChat Pay, Alipay) and browser-assisted exports gathered through `/cfo-statement-export`. Use when importing bank or credit card CSV exports. CLEAR step: C (Capture)
Build and execute commands to obtain Japanese stock market data with J-Quants CLI (jquants). It supports acquisition of stock prices, OHLCV candlesticks, minute bars, tick data, margin trading balances, short selling ratios, futures, options, financial statements, dividends, TOPIX and index data. It also supports checking API availability and obtainable data periods (Free/Light/Standard/Premium) for each plan. Triggers: jquants, J-Quants, stock price, stock price data, ticker, OHLCV, minute bar, JPX, TSE, TOPIX, Nikkei 225, futures, options, derivatives, short selling, margin trading, dividend, financial statements, financial results, PER, PBR, ROE, index, bulk download, Japanese stock, market data, subscription, plan, data range, API availability, update time, data update, update timing, outdated data, latest data, data freshness, update schedule
Gravity and magnetic data processing and forward modelling using Fatiando a Terra. Use when Claude needs to: (1) Compute gravity forward models (point masses, prisms, tesseroids), (2) Apply terrain/Bouguer corrections, (3) Grid scattered potential field data with equivalent sources, (4) Perform upward/downward continuation, (5) Calculate magnetic anomalies from magnetized bodies, (6) Apply derivative filters (gradients, tilt angle), (7) Process regional or local gravity surveys.
Spatial data processing for geological modelling with GemPy. Use when Claude needs to: (1) Prepare spatial data for GemPy models, (2) Extract interface points from geological maps, (3) Process orientations/dip measurements, (4) Sample DEMs along profiles or cross-sections, (5) Convert between GIS formats and GemPy inputs, (6) Clip/transform vector/raster data for modeling, (7) Create model extents from geospatial bounds.
Read and parse DLIS (Digital Log Interchange Standard) and LIS (Log Information Standard) well log files. Use when Claude needs to: (1) Read/parse DLIS or LIS files, (2) Extract well log curves as numpy arrays, (3) Access file metadata and origin information, (4) Handle multi-frame or multi-file DLIS, (5) Convert DLIS to LAS or DataFrame, (6) Work with RP66 format well logs, (7) Process array or image log data.
Compute surface wave dispersion curves for layered Earth models using the Thomson-Haskell matrix method with Numba acceleration. Use when Claude needs to: (1) Calculate Rayleigh or Love wave phase velocities, (2) Compute group velocity dispersion, (3) Generate sensitivity kernels for inversion, (4) Forward model dispersion curves from velocity profiles, (5) Compare dispersion between different Earth models, (6) Set up surface wave tomography workflows.